Papers › BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations

BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations

20 Dec 2021arXiv:2112.10716archive 2025-07-28

Bruno Artacho, Andreas Savakis

We propose BAPose, a novel bottom-up approach that achieves state-of-the-art results for multi-person pose estimation. Our end-to-end trainable framework leverages a disentangled multi-scale waterfall architecture and incorporates adaptive convolutions to infer keypoints more precisely in crowded scenes with occlusions. The multi-scale representations, obtained by the disentangled waterfall module in BAPose, leverage the efficiency of progressive filtering in the cascade architecture, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Our results on the challenging COCO and CrowdPose datasets demonstrate that BAPose is an efficient and robust framework for multi-person pose estimation, achieving significant improvements on state-of-the-art accuracy.

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Tasks

Multi-Person Pose EstimationPose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Multi-Person Pose Estimation COCO (Common Objects in Context) BAPose AP 0.727 #5 of 15 Archive leaderboard report
Multi-Person Pose Estimation COCO (Common Objects in Context) BAPose Test AP 71.2 #5 of 15 Archive leaderboard report
Multi-Person Pose Estimation COCO (Common Objects in Context) BAPose Validation AP 72.7 #5 of 15 Archive leaderboard report
Multi-Person Pose Estimation CrowdPose BAPose (W32) AP Easy 79.9 #8 of 28 Archive leaderboard report
Multi-Person Pose Estimation CrowdPose BAPose (W32) AP Hard 61.3 #8 of 28 Archive leaderboard report
Multi-Person Pose Estimation CrowdPose BAPose (W32) AP Medium 73.4 #8 of 28 Archive leaderboard report
Multi-Person Pose Estimation CrowdPose BAPose (W32) mAP @0.5:0.95 72.2 #8 of 28 Archive leaderboard report

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